For each language, we have a set of untranscribed data that primarily consists of BN data that matches the target domain.
The focal point of this work is using this untranscribed data for semisupervised training [1]} to overcome domain mismatch.
We first use the relatively simple approach of self-training [2]}, [3]}.
In self-training, transcribed data is used to train an initial seed model, which is used for decoding the untranscribed data to generate pseudotranscripts.
These psuedotranscripts are treated as true transcripts and combined with the original data for retraining the acoustic models.
Previous work has demonstrated the effectiveness of semisupervised training for domain adaptation [4]}.
